Generalized fluctuation test for deciphering phenotypic switching within cell populations

破译细胞群内表型转换的广义波动测试 生化反应固有的概率性质与低拷贝数成分相结合，导致显着的 单个细胞内 mRNA/蛋白质水平的随机波动（噪声）。细胞生化过程如何发挥作用 面对这种随机性，如何可靠地解决问题是一个有趣的基本问题。我们实验室的长期愿景是开发 用于研究细胞生化过程的随机动力学的新数学和计算工具，并使用这些工具 系统地了解噪声如何影响生物功能和表型的工具。由于基因噪音 在产品水平上，同克隆群体中的单细胞的表达谱可能不同，并且存在不同的表型。 典型状态。这种细胞间变异的动态性质，其中单个细胞可以在不同的细胞之间转换 随着时间的推移，这种现象变得特别难以描述。出乎意料的是，表型异质性 群体内的基因可以在不同的生物过程中发挥重要的功能作用，从驱动遗传相同的 细胞具有不同的细胞命运，允许微生物和癌细胞对冲不确定的环境变化。 75 年前推出的 Luria-Delbrück 实验，也称为“波动测试”，证明了基因 mu- 在没有选择的情况下随机出现——而不是对选择的反应——并导致了诺贝尔奖。的 该项目的创新之处在于利用这一经典实验与数学建模相结合来描述 表征细胞状态之间的可逆和不可逆切换。该方法的主要优点是 足够通用，足以应用于任何增殖细胞类型，并且仅涉及进行单个端点测量。这个 对于测量涉及杀死细胞的情况尤其重要（例如，检测细菌是否 真实细胞处于药物敏感或耐药状态或正在进行RNA测序），因此同一细胞的状态不能 在不同的时间点进行测量。该项目将开发用于表征表型转换的数学工具 使用波动测试在任意数量的状态之间进行分析，并且此类技术将首次区分 通过遗传改变进行的不可逆细胞状态转变与可逆表观遗传转变之间的关系。这些工具 将首先使用计算机生成的数据进行基准测试，然后应用于调查不同问题的实验数据集 lems，包括表征细菌/真菌细胞的耐药状态，了解病毒易感性的差异 在同一克隆群内的单个人类细胞之间进行研究，并揭示干细胞状态的瞬时动态 使单个细胞偏向不同的分化命运。我们的初步工作揭示了耐药状态的可塑性 在具有不同遗传时间尺度的细菌、真菌和癌细胞中。为了了解细胞状态的起源， 该项目将开发计算工具，用于从单细胞表达数据推断因果相互作用网络。这些 工具将揭示网络拓扑如何跨细胞状态变化，并对底层的随机动态进行建模 生化网络将机械地捕捉状态之间的转变。总体而言，通过该项目开发的工具 将导致对随机表观遗传过程如何产生单细胞差异的基本理解 无需对 DNA 进行任何改变，并推动转化方法扰乱细胞状态以获得治疗效果。

Generalized fluctuation test for deciphering phenotypic switching within cell populations The inherent probabilistic nature of biochemical reactions coupled with low-copy number components results in significant random fluctuations (noise) in mRNA/protein levels inside individual cells. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. A long-term vision of our lab is to develop new mathematical and computational tools for studying stochastic dynamics of cellular biochemical processes, and use these tools to systematically understand how noise affects biological function and phenotype. As a consequence of noise in gene product levels, single cells within an isoclonal population can differ in their expression profile and reside in different pheno- typic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Unexpectedly, phenotypic heterogeneity within a population can play important functional roles in diverse biological processes, from driving genetically-identical cells to different cell fates to allowing microbes and cancer cells to hedge their bets against uncertain environmental changes. The Luria-Delbrück experiment, also called the “Fluctuation Test", introduced 75 years ago, demonstrated that genetic mu- tations arise randomly in the absence of selection – rather than in response to selection – and led to a Nobel Prize. The innovation of this project is to leverage this classical experiment in conjunction with mathematical modeling to char- acterize reversible and irreversible switching between cell states. The key advantage of the proposed method is that it is general enough to be applied to any proliferating cell type, and only involves making a single endpoint measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, assaying whether a bacte- rial cell is in a drug-sensitive or drug-tolerant state or doing RNA-sequencing), and hence the state of the same cell cannot be measured at different time points. The project will develop mathematical tools for characterizing phenotypic switching between an arbitrary number of states using the fluctuation test, and such techniques will for the first time differentiate between an irreversible cell-state transition via genetic alterations vs. a reversible epigenetic transition. These tools will be first benchmarked with in-silico generated data and then applied on experimental datasets investigating diverse prob- lems, including characterizing drug-tolerant states in bacterial/fungal cells, understanding differences in viral susceptibility between single human cells within the same clonal population, and uncovering the transient dynamics of stem cell states that bias individual cells to different differentiation fates. Our preliminary work reveals plasticity in drug-tolerant states in bacterial, fungal, and cancer cells with different inheritance timescales. To understand the origins of cell states, the project will develop computational tools for inferring causal interaction networks from single-cell expression data. These tools will uncover how network topologies change across cell states and modeling the stochastic dynamics of underlying biochemical networks will mechanistically capture transitions between states. Overall, tools developed through this project will result in a fundamental understanding of how single-cell difference arises from stochastic epigenetic processes without any changes to DNA, and drive translational approaches to perturb cell states for therapeutic benefit.